Association between Removal of High-Frequency Oscillations and the Effect of Epilepsy Surgery: A Meta-Analysis

Keywords

high-frequency oscillations - intractable epilepsy - intracranial electrography - epilepsy prognosis - electrocorticogram

Introduction

The aim of an epilepsy surgery is to resect the brain tissue responsible for seizure generation, known as the epileptogenic zone (EZ). Prior to surgery, the brain tissue undergoes evaluation to identify the seizure onset zone (SOZ), which refers to the cortical region that initiates seizures. Accurate identification of the EZ holds the potential to improve patient prognosis.

In recent years, high-frequency oscillations (HFOs) have emerged as a promising marker for epileptogenicity. HFOs can be categorized into two frequency ranges: “ripples” (80–250 Hz) and “fast ripples” (FR; 250–500 Hz).[1] HFOs have demonstrated superior effectiveness in specifically identifying the SOZ compared with spikes.[2] [3] [4] [5] Previous studies have consistently shown a strong association between HFOs and the SOZ,[6] [7] [8] surpassing that of interictal epileptiform discharges.[9] Significant disparities in HFOs rates and amplitudes have been observed between the SOZ and non-SOZ regions in various brain lobes.[10] [11] Hence, HFOs play a crucial role in the detection and delineation of epileptic regions.

Several studies have established a close relationship between HFOs and surgical outcomes. Complete removal of HFO-producing brain regions has been linked to favorable postoperative outcomes,[12] [13] [14] while incomplete removal correlated with epileptic recurrence.[4] [15] [16] [17] [18]

However, no significant distinction has been found in the delineation of the SOZ using HFOs instead of spikes.[19] [20] Gloss et al's meta-analysis of two prospective studies did not yield a significant improvement in postoperative seizure outcomes with the use of HFOs.[21] Similarly, Höller et al reported minor differences in their meta-analysis of HFOs pertaining to epilepsy outcomes.[22] Therefore, the reliability of HFOs as a biomarker for epileptic tissue remains a subject of ongoing debate.

HFOs can be monitored using scalp electroencephalogram (EEG), electrocorticogram (ECOG), and stereoelectroencephalography (SEEG). Scalp EEG is prone to artifacts and errors due to slight movements of the monitored individual, making EZ delineation challenging. ECOG, consisting of a grid or strip electrode that covers the brain cortex, offers denser contacts and fewer artifacts compared with scalp EEG.

SEEG, on the other hand, allows for monitoring of electrical activities in deep brain structures over a wider range. It provides detailed recordings of the origin and transmission of epileptic discharges, facilitating a better understanding of the transmission network of the cerebral cortex and accurate EZ delineation.[23] [24] [25] In patients in whom noninvasive methods fail to satisfactorily identify epileptic tissue, intracranial electroencephalography (iEEG) monitoring serves as the gold standard for EZ delineation.[26] [27] [28]

We conducted a meta-analysis to evaluate the association between postoperative efficacy and HFOs monitored using ECOG and SEEG.

Methods

Inclusion Criteria

The inclusion criteria were the following: prospective or retrospective studies investigating surgical outcomes in patients with intractable epilepsy using iEEG monitoring and studies providing information on the proportion of HFO resection and patient prognosis. Studies were excluded if they involved duplicate patient records or were not written in English.

Study Search

Two independent reviewers conducted a comprehensive search of PubMed and Cochrane databases up to January 2023, using the following retrieval strategy:

• PubMed: ((high frequency oscillations) OR (fast oscillations) OR (ripples)) AND ((epilepsy) OR (seizure)) AND ((surgery) OR (operation)).

• Cochrane: (high frequency oscillations) OR (fast oscillations) OR (ripples) AND (epilepsy) OR (seizure) AND (surgery) OR (operation).

Data Extraction

The following information was extracted by the author: type of iEEG used (ECOG or SEEG), patient characteristics including mean age at the time of surgery, sample size, sex ratio, mean follow-up time, and recorded outcomes. Additionally, details regarding the type of HFOs (ripples or FR), analysis of HFOs (visual or automatic), and the proportion of HFOs removed were collected.

Quality Assessment

The quality of the included studies was evaluated by two reviewers using Cochrane's risk of bias tool.[29] In cases of disagreement, a third author was consulted to reach a resolution. The following aspects were assessed for risk of bias, categorized as low, high, or unclear: age at surgery, iEEG methodology, detection and thresholding of HFOs, sample size, representation of females, follow-up duration, and recorded outcomes.

Data Classification and Analyses

The patients from the included studies were categorized into four groups based on the extent of HFOS removal: completely removed FR (C-FR), completely removed ripples (C-Ripples), mostly removed FR (P-FR), and mostly removed ripples (P-Ripples). A meta-analysis was conducted to investigate the relationship between seizure outcomes and the proportion of removed HFOs. However, some studies in the P-Ripples and P-FR groups did not provide specific criteria for determining the majority of HFOs removed.

To define the majority of HFOs removed, we applied the equation listed below.[13] The criteria were as follows: if the value of RatioChanns (ev) was ≥0 and ≤1, it was classified as the majority of HFOs removed; otherwise, it was classified as the majority of HFOs untouched.

Here, ev represented HFOs (FR or ripples), ChannRem represented the channels of events that had been removed, ChannNonRem represented the channels of events that had not been removed, and RatioChanns(ev) represented the difference between the number of removed channels (ev) and the number of unremoved channels (ev), divided by the total number of channels (ev). The value of RatioChanns(ev) fell between 1 and –1. A value between 0 and 1 (including 0) indicated that the majority of events were removed, while a value between –1 and 0 (excluding 0) indicated that the majority of events were untouched.

Statistical Analyses

Review Manager 5.4 software was used to analyze the collected data. Patients without either ripples or FR were excluded from their respective studies. The assumption of heterogeneity was assessed using the chi-square-based Q test. A p value of ≥0.10 indicated a lack of heterogeneity among the studies. The fixed-effects model was used to calculate the odds ratio (OR) and 95% confidence interval (CI).

Results

Study Screening Results

A total of 216 records were retrieved from PubMed and 8 records were obtained from the Cochrane Library, all of which met the inclusion criteria. After reviewing the titles and abstracts, 20 studies were selected for further evaluation ([Fig. 1]). However, only 10 studies provided the necessary data upon full-text review. It should be noted that one study by Hussain et al[30] was excluded due to patient overlap with their previous study,[31] which we already included. Consequently, a total of nine studies were included in this meta-analysis.

Fig. 1 Flow diagram of study selection. HFOs, high-frequency oscillations.

Quality Assessment

The Cochrane risk of bias assessment[32] [33] revealed that none of the included studies reported random allocation, allocation concealment, or implementation of blinding methods.

Clinical Characteristics

Most of the included studies provided demographic information, such as the age and gender of the patients (see [Table 1]). Four studies exclusively involved minors.[31] [34] One study did not specify the follow-up time,[34] while another study reported a small number of patients lost to follow-up.[35] Additionally, one study did not mention the criteria for prognostic assessment,[35] but defined seizure freedom at each follow-up visit as the absence of breakthrough seizures since the last clinical visit. For our analysis, we defined seizure freedom based on Engel I or International League Against Epilepsy I (ILAE I) classification. A study conducted by Fedele et al[36] evaluated the efficacy of C-FR and ripples, providing data on the removed HFOs for each patient. Hence, we were able to classify their patients into the P-Ripples or P-FR group.

Table 1 Basic information of the included research literature

Study	Age at surgery (y)	iEEG	Detection	Threshold	HFOs	Sample	Female	Follow-up	Outcome
Akiyama et al[41]	10.68 ± 5.0	SEEG/ECoG	Auto	Bootstrapping	R/FR	28	–	≥2 y	LIAE
Fujiwara, 2012	–	ECoG	Auto	1/min	FR	41	–	≥12 mo	–
van Klink, 2014	18.43 ± 10.39	ECoG	Auto	1/min	R/FR	14	7	≥12 mo	Engel
Okanishi et al[40]	9.35 ± 5.26	ECoG	Auto	Bootstrapping	R/FR	10	4	≥19 mo	Engel
Fujiwara et al[34]	5.96 ± 4.43	ECoG	–	–	R/FR	14	7	–	LIAE
Hussain et al[31]	8.93 ± 5.34	ECoG	Visually	–	FR	30	15	52.84 ± 26.29 mo	–
Fedele et al[36]	32.1 ± 11.5	SEEG/ECoG	Auto	95%	R/FR	20	6	25.1 ± 12.5 mo	ILAE
Jacobs et al[39]	23.2 ± 17.2	SEEG/ECoG	Visually/auto	–	R/FR	52	34	≥12 mo	Engel
Nariai et al[35]	13.1 ± 5.4	SEEG/ECoG	Visually	–	FR	19	10	–	–

Abbreviations: ECoG, electrocorticogram; FR, fast ripples; HFO, high-frequency oscillation; iEEG, intracranial electroencephalography; ILAE, International League Against Epilepsy; R, ripples; SEEG, stereoelectroencephalography.

Group Classification Based on Removed Ratio

The distribution of studies among the groups was as follows: 8 studies in the C-FR group, 5 studies in the C-Ripples group, 5 studies in the majority removed FR group, and 5 studies in the majority removed ripples group.

Completely Removed FR and Prognosis

[Fig. 2] illustrates the meta-analysis results regarding the relationship between C-FR and patient prognosis. The fixed-effects model was utilized, and the heterogeneity test indicated no significant heterogeneity (I ² = 0%; p = 0.68). The estimated model yielded a significant result, indicating that patients in the C-FR group had a significantly higher prognosis compared with those with incomplete removal (OR = 6.62; 95% CI: 3.10–14.15; p ≤ 0.00001).

Fig. 2 Completely removed fast ripples (FR) and outcome. In seven studies, the confidence intervals (CIs) overlap with 0, indicating no clear difference in outcome between patients based on these seven studies alone. However, the pooled meta-analysis demonstrates a significant positive difference that does not overlap with 0. Therefore, it can be concluded that patients with completely removed FR have a better prognosis compared with those with incompletely removed FR (odds ratio [OR] = 6.62; 95% confidence interval [CI]: 3.10–14.15; p < 0.00001).

The funnel plot, displayed in [Fig. 3], exhibits symmetrical distribution of points along the center line and both sides, suggesting no noticeable publication bias among the included studies.

Fig. 3 Funnel plot. OR, odds ratio; SE, standard error.

Completely Removed Ripples and Prognosis

[Fig. 4] presents the meta-analysis results concerning the association between C-Ripples and patient prognosis in the C-Ripples group. The fixed-effects model was employed, and the heterogeneity test indicated no significant heterogeneity (I ² = 0%; p = 0.59). In the C-Ripples group, the estimated model yielded a significant result (OR = 4.45; 95% CI: 1.33–14.89; p = 0.02). Patients with C-Ripples demonstrated a significantly better prognosis compared with those with incomplete removal.

Fig. 4 Completely removed ripples and outcome. In four studies, the confidence intervals (CIs) overlap with 0, suggesting no clear difference in outcome between patients based on these four studies individually. However, the pooled meta-analysis reveals a positive difference without overlap with 0, indicating that the prognosis of patients with completely removed ripples is better than that of patients with incompletely removed ripples (odds ratio [OR] = 4.45; 95% CI: 1.33–14.89; p = 0.02).

Majority of FR Removed and Prognosis

Data regarding the majority of FR removed was obtained from five studies. Since Fedele et al[36] did not specify the criteria for the majority removed FR, we applied the equation listed earlier[13] to define the majority removed FR. [Fig. 5] presents the meta-analysis results regarding the relationship between the majority of FR removed and patient prognosis in the P-FR group.

Fig. 5 Majority of fast ripples (FR) removed and outcome. In three studies, the confidence intervals (CIs) overlap with 0, indicating no clear difference in outcome between patients based on these three studies alone. Nevertheless, the pooled meta-analysis demonstrates a significant positive difference that does not overlap with 0. Thus, it can be concluded that the prognosis of patients with the majority of FR removed is better than that of patients with the majority of FR untouched (odds ratio [OR] = 6.23; 95% CI: 2.04–19.06; p = 0.001).

The fixed-effects model was utilized, and the heterogeneity test indicated no significant heterogeneity (I ² = 0%; p = 0.68). The estimated model yielded a significant result, indicating that patients with the majority of FR removed had a significantly higher prognosis compared with those with the majority of FR untouched (OR = 6.23; 95% CI: 2.04–19.06; p = 0.001).

Majority of Ripples Removed and Prognosis

Similar to the previous analysis, data on the majority of ripples removed were obtained from the aforementioned study. The same method was applied to the data from Fedele et al[36] to define the criteria for the majority of ripples removed. [Fig. 6] depicts the meta-analysis results for the majority of ripples removed and patient prognosis in the P-Ripples group. The fixed-effects model was used, and the heterogeneity test indicated no significant heterogeneity (I ² = 0%; p = 0.67). The estimated model yielded a significant result, showing that patients with the majority of ripples removed had a significantly better prognosis compared with those with the majority of ripples untouched (OR = 8.14; 95% CI: 2.62–25.33; p = 0.0003). The seizure-free ratio was higher in the majority of ripples removed groups than in the majority of ripples untouched groups.

Fig. 6 Majority of ripples removed and outcome. In three studies, the confidence intervals (CIs) overlap with 0, suggesting no clear difference in outcome between patients based on these three studies individually. However, the pooled meta-analysis reveals a positive difference without overlap with 0, indicating that the prognosis of patients with the majority of ripples removed is better than that of patients with the majority of ripples untouched (odds ratio [OR] = 8.14; 95% CI: 2.62–25.33; p = 0.0003).

Discussion

This study included a total of nine studies involving 228 patients, which were categorized into four groups based on the removal of HFOs: C-FR, C-Ripples, P-FR, and P-Ripples. The estimated model for all groups revealed that removing a larger region of HFOs could significantly enhance patient prognosis.

Prospective Studies

Currently, there is no standardized method for HFO detection, and interrater reliability remains a major challenge.[37] [38] Notably, a previous meta-analysis by Gloss et al[21] that included two prospective studies reported no evidence supporting the use of HFOs to improve the efficacy of epileptic surgery. However, these findings are inconsistent with previous studies, likely due to variations in evaluation criteria and analysis methods for HFOs. In a recent prospective multicenter study by Jacobs et al,[39] which was one of the studies included in our analysis, complete removal of HFOs did not demonstrate a significant association with the efficacy of epileptic surgery. Nevertheless, their data from a single center suggested that completely removing HFOs was more likely to improve patient outcomes, consistent with their previous studies. The consistent positive results from this single-center study may be attributed to Jacobs et al[39] using uniform criteria for recording and analyzing HFOs. Therefore, conducting prospective studies using consistent methods could yield more reliable and significant results.

A study by Nariai et al,[35] also included in our analysis, was the first prospective study to provide evidence linking complete removal of visually recognized FR in ECoG with postoperative prognosis in epilepsy patients, supporting the findings of our meta-analysis. However, it is worth mentioning that similar prospective studies have included a limited number of cases, which reduces the credibility of their results. This limitation mainly stems from the high technical requirements and economic costs associated with these studies. Therefore, in the future, conducting more prospective studies with consistent criteria, either through a multicenter collaboration or systematic evaluations, would be beneficial in reducing heterogeneity among studies.

Heterogeneity

Studies by Okanishi et al[40] and Fujiwara et al[34] focused on minors with multiple tuberous sclerosis, while Akiyama et al[41] and Hussain et al[31] also exclusively included minors. Subgroup analysis of these four studies could be conducted if necessary to explore the relationship between HFOs and surgical efficacy in patients with multiple tuberous sclerosis complex or intractable epilepsy among minors. In a prospective multicenter study by Jacobs et al,[39] artificial visual recognition of HFOs was employed to test the effectiveness of automatic HFO detection. However, visual recognition is subjective, and the reliability of automatic HFO detection remains a subject of debate. Therefore, further studies are warranted to investigate this aspect.

Automated Detection

In recent years, many researchers have shifted toward the use of newly developed automatic detection methods instead of visual detection. Höller et al[22] discovered that automatic detection outperforms visual detection. Moreover, when it comes to detecting HFOs in the operating room immediately after resection, automatic detection becomes essential. However, it should be noted that many detectors require offline processing of recorded signals to apply automatic or semiautomatic artifact elimination stages for the removal of events incorrectly classified as HFOs. Therefore, the development of automatic detection has become increasingly significant.

To comprehend the superiority of automatic detection over visual detection, researchers are increasingly emphasizing the need to compare the relationship between HFO removal and outcomes to verify the efficiency of the detectors in reverse. Previous studies have not established a clear relationship between the two. However, Burelo et al[18] proposed a detector based on a spiking neural network (SNN) and spectral analysis to explore this relationship, offering the possibility of real-time detection of HFOs during epilepsy surgery.

Predicting Individual Outcome

Although the analysis at the group level revealed a correlation between the removal of HFO-generating tissue and seizure freedom, it should be noted that accurate prediction of patient outcomes based on HFO removal is limited to a subset of patients.[13] [14] In contrast to population-level results, it has been observed that HFOs are not consistently effective in identifying seizures at an individual patient level, as they exhibit similarities to spikes.[19] This discrepancy may arise due to the challenge of differentiating physiological HFOs from pathological HFOs. There is significant frequency overlap between these two types of HFOs,[42] [43] [44] [45] and relying solely on frequency and amplitude analysis is inadequate for distinguishing between them.[46] [47] Therefore, achieving a better distinction between physiological and pathological HFOs may enhance the ability to predict the prognosis of individual patients.

Postoperative residual HFOs also hold importance in predicting the prognosis of individual patients. Many researchers have emphasized the significance of using residual HFOs to predict epilepsy outcomes. They have found that residual HFOs observed in ECoG recordings may provide more reliable predictions of seizure outcomes compared with preoperative HFOs.[4] [48]

Conclusion

Our study reveals a significant correlation between the removal of HFOs and achieving seizure freedom. Specifically, the removal of a larger region containing HFOs is associated with favorable postoperative seizure outcomes. This meta-analysis highlights the crucial role of HFOs in identifying the EZ and guiding surgical strategies. However, to validate these findings, it is imperative to conduct prospective multicenter studies with standardized protocols and methods. Furthermore, additional research is needed to investigate the impact of residual HFOs on seizure outcomes. The efficacy of residual HFOs relies on the development of more advanced techniques for automatic intraoperative detection of HFOs. Hence, the future advancement of automatic intraoperative detectors holds great importance in this regard.

• References

• 1 Bragin A, Engel Jr J, Wilson CL, Fried I, Buzsáki G. High-frequency oscillations in human brain. Hippocampus 1999; 9 (02) 137-142
• 2 Jacobs J, Levan P, Châtillon CE, Olivier A, Dubeau F, Gotman J. High frequency oscillations in intracranial EEGs mark epileptogenicity rather than lesion type. Brain 2009; 132 (Pt 4): 1022-1037
• 3 Zijlmans M, Jiruska P, Zelmann R, Leijten FS, Jefferys JG, Gotman J. High-frequency oscillations as a new biomarker in epilepsy. Ann Neurol 2012; 71 (02) 169-178
• 4 van 't Klooster MA, van Klink NEC, Zweiphenning WJEM. et al. Tailoring epilepsy surgery with fast ripples in the intraoperative electrocorticogram. Ann Neurol 2017; 81 (05) 664-676
• 5 van Klink NEC, Zweiphenning WJEM, Ferrier CH. et al. Can we use intraoperative high-frequency oscillations to guide tumor-related epilepsy surgery?. Epilepsia 2021; 62 (04) 997-1004
• 6 Liu S, Gurses C, Sha Z. et al. Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy. Brain 2018; 141 (03) 713-730
• 7 Zijlmans M, Jacobs J, Kahn YU, Zelmann R, Dubeau F, Gotman J. Ictal and interictal high frequency oscillations in patients with focal epilepsy. Clin Neurophysiol 2011; 122 (04) 664-671
• 8 Urrestarazu E, Jirsch JD, LeVan P. et al. High-frequency intracerebral EEG activity (100-500 Hz) following interictal spikes. Epilepsia 2006; 47 (09) 1465-1476
• 9 Ren L, Kucewicz MT, Cimbalnik J. et al. Gamma oscillations precede interictal epileptiform spikes in the seizure onset zone. Neurology 2015; 84 (06) 602-608
• 10 Guragain H, Cimbalnik J, Stead M. et al. Spatial variation in high-frequency oscillation rates and amplitudes in intracranial EEG. Neurology 2018; 90 (08) e639-e646
• 11 Schönberger J, Huber C, Lachner-Piza D. et al. Interictal fast ripples are associated with the seizure-generating lesion in patients with dual pathology. Front Neurol 2020; 11: 573975
• 12 Wu JY, Sankar R, Lerner JT, Matsumoto JH, Vinters HV, Mathern GW. Removing interictal fast ripples on electrocorticography linked with seizure freedom in children. Neurology 2010; 75 (19) 1686-1694
• 13 Jacobs J, Zijlmans M, Zelmann R. et al. High-frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery. Ann Neurol 2010; 67 (02) 209-220
• 14 Haegelen C, Perucca P, Châtillon CE. et al. High-frequency oscillations, extent of surgical resection, and surgical outcome in drug-resistant focal epilepsy. Epilepsia 2013; 54 (05) 848-857
• 15 Boran E, Ramantani G, Krayenbühl N. et al. High-density ECoG improves the detection of high frequency oscillations that predict seizure outcome. Clin Neurophysiol 2019; 130 (10) 1882-1888
• 16 Fedele T, Ramantani G, Burnos S. et al. Prediction of seizure outcome improved by fast ripples detected in low-noise intraoperative corticogram. Clin Neurophysiol 2017; 128 (07) 1220-1226
• 17 Weiss SA, Berry B, Chervoneva I. et al. Visually validated semi-automatic high-frequency oscillation detection aides the delineation of epileptogenic regions during intra-operative electrocorticography. Clin Neurophysiol 2018; 129 (10) 2089-2098
• 18 Burelo K, Sharifshazileh M, Krayenbühl N, Ramantani G, Indiveri G, Sarnthein J. A spiking neural network (SNN) for detecting high frequency oscillations (HFOs) in the intraoperative ECoG. Sci Rep 2021; 11 (01) 6719
• 19 Roehri N, Pizzo F, Lagarde S. et al. High-frequency oscillations are not better biomarkers of epileptogenic tissues than spikes. Ann Neurol 2018; 83 (01) 84-97
• 20 Roehri N, Bartolomei F. Are high-frequency oscillations better biomarkers of the epileptogenic zone than spikes?. Curr Opin Neurol 2019; 32 (02) 213-219
• 21 Gloss D, Nevitt SJ, Staba R. The role of high-frequency oscillations in epilepsy surgery planning. Cochrane Database Syst Rev 2017; 10 (10) CD010235
• 22 Höller Y, Kutil R, Klaffenböck L. et al. High-frequency oscillations in epilepsy and surgical outcome. A meta-analysis. Front Hum Neurosci 2015; 9: 574
• 23 Mo JJ, Hu WH, Zhang C. et al. Value of stereo-electroencephalogram in reoperation of patients with pharmacoresistant epilepsy: a single center, retrospective study. Br J Neurosurg 2018; 32 (06) 663-670
• 24 Ollivier I, Behr C, Cebula H. et al. Efficacy and safety in frameless robot-assisted stereo-electroencephalography (SEEG) for drug-resistant epilepsy. Neurochirurgie 2017; 63 (04) 286-290
• 25 Goldstein HE, Youngerman BE, Shao B. et al. Safety and efficacy of stereoelectroencephalography in pediatric focal epilepsy: a single-center experience. J Neurosurg Pediatr 2018; 22 (04) 444-452
• 26 Bruder JC, Schmelzeisen C, Lachner-Piza D, Reinacher P, Schulze-Bonhage A, Jacobs J. Physiological ripples associated with sleep spindles can be identified in patients with refractory epilepsy beyond mesio-temporal structures. Front Neurol 2021; 12: 612293
• 27 Rosenow F, Lüders H. Presurgical evaluation of epilepsy. Brain 2001; 124 (Pt 9): 1683-1700
• 28 Blount JP, Cormier J, Kim H, Kankirawatana P, Riley KO, Knowlton RC. Advances in intracranial monitoring. Neurosurg Focus 2008; 25 (03) E18
• 29 Higgins JP, Altman DG, Gøtzsche PC. et al; Cochrane Bias Methods Group; Cochrane Statistical Methods Group. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011; 343: d5928
• 30 Hussain SA, Mathern GW, Hung P, Weng J, Sankar R, Wu JY. Intraoperative fast ripples independently predict postsurgical epilepsy outcome: comparison with other electrocorticographic phenomena. Epilepsy Res 2017; 135: 79-86
• 31 Hussain SA, Mathern GW, Sankar R, Wu JY. Prospective and “live” fast ripple detection and localization in the operating room: impact on epilepsy surgery outcomes in children. Epilepsy Res 2016; 127: 344-351
• 32 Sterne JA, Egger M, Smith GD. Systematic reviews in health care: Investigating and dealing with publication and other biases in meta-analysis. BMJ 2001; 323 (7304) 101-105
• 33 Tridente A, De Martino L, De Luca D. Porcine vs bovine surfactant therapy for preterm neonates with RDS: systematic review with biological plausibility and pragmatic meta-analysis of respiratory outcomes. Respir Res 2019; 20 (01) 28
• 34 Fujiwara H, Leach JL, Greiner HM. et al. Resection of ictal high frequency oscillations is associated with favorable surgical outcome in pediatric drug resistant epilepsy secondary to tuberous sclerosis complex. Epilepsy Res 2016; 126: 90-97
• 35 Nariai H, Hussain SA, Bernardo D. et al. Prospective observational study: fast ripple localization delineates the epileptogenic zone. Clin Neurophysiol 2019; 130 (11) 2144-2152
• 36 Fedele T, Burnos S, Boran E. et al. Resection of high frequency oscillations predicts seizure outcome in the individual patient. Sci Rep 2017; 7 (01) 13836
• 37 Frauscher B, Bartolomei F, Kobayashi K. et al. High-frequency oscillations: the state of clinical research. Epilepsia 2017; 58 (08) 1316-1329
• 38 Spring AM, Pittman DJ, Aghakhani Y. et al. Interrater reliability of visually evaluated high frequency oscillations. Clin Neurophysiol 2017; 128 (03) 433-441
• 39 Jacobs J, Wu JY, Perucca P. et al. Removing high-frequency oscillations: a prospective multicenter study on seizure outcome. Neurology 2018; 91 (11) e1040-e1052
• 40 Okanishi T, Akiyama T, Tanaka S. et al. Interictal high frequency oscillations correlating with seizure outcome in patients with widespread epileptic networks in tuberous sclerosis complex. Epilepsia 2014; 55 (10) 1602-1610
• 41 Akiyama T, McCoy B, Go CY. et al. Focal resection of fast ripples on extraoperative intracranial EEG improves seizure outcome in pediatric epilepsy. Epilepsia 2011; 52 (10) 1802-1811
• 42 Engel Jr J, Bragin A, Staba R, Mody I. High-frequency oscillations: what is normal and what is not?. Epilepsia 2009; 50 (04) 598-604
• 43 Matsumoto A, Brinkmann BH, Matthew Stead S. et al. Pathological and physiological high-frequency oscillations in focal human epilepsy. J Neurophysiol 2013; 110 (08) 1958-1964
• 44 Pail M, Cimbálník J, Roman R. et al. High frequency oscillations in epileptic and non-epileptic human hippocampus during a cognitive task. Sci Rep 2020; 10 (01) 18147
• 45 Brázdil M, Cimbálník J, Roman R. et al. Impact of cognitive stimulation on ripples within human epileptic and non-epileptic hippocampus. BMC Neurosci 2015; 16: 47
• 46 Pail M, Řehulka P, Cimbálník J, Doležalová I, Chrastina J, Brázdil M. Frequency-independent characteristics of high-frequency oscillations in epileptic and non-epileptic regions. Clin Neurophysiol 2017; 128 (01) 106-114
• 47 Nagasawa T, Juhász C, Rothermel R, Hoechstetter K, Sood S, Asano E. Spontaneous and visually driven high-frequency oscillations in the occipital cortex: intracranial recording in epileptic patients. Hum Brain Mapp 2012; 33 (03) 569-583
• 48 van 't Klooster MA, van Klink NE, Leijten FS. et al. Residual fast ripples in the intraoperative corticogram predict epilepsy surgery outcome. Neurology 2015; 85 (02) 120-128